Search Results (177)

This paper proposes AutoRL, a high-fidelity Robot Operating System 2 (ROS2)–Gazebo simulation pipeline that addresses a critical reproducibility gap in learning-based flight control: existing reinforcement learning (RL) frameworks for unmanned aerial vehicles (UAVs) lack deterministic, step-level coupling between control actions and physics updates. AutoRL enforces a strict one-to-one correspondence between agent actions and physics updates via blocking ROS2 service calls, preserving the Markov property required for stable policy learning and enabling verifiable reproducibility independent of the learning algorithm. A composite reward function jointly optimizes attitude tracking accuracy, oscillation suppression, actuator smoothness, and disturbance robustness. Its modular, service-oriented architecture provides a reusable framework for offline-trained RL research. A proximal policy optimization (PPO) controller trained within AutoRL validates the framework, demonstrating consistent convergence and stable performance across multiple independently seeded runs. Determinism was experimentally verified across two regimes: with Gaussian IMU noise disabled, repeated rollouts produced bit-identical trajectories, while with noise enabled, the measured distribution of trajectory divergence agreed with a reference distribution drawn from the declared sensor model, together confirming that the blocking service call architecture eliminates all non-stochastic sources of nondeterminism between the agent and the physics engine. The trained model is exported in a lightweight form compatible with embedded flight control firmware and remains adaptable across airframe configurations by automatically recomputing the control allocation matrix from configuration files. The total policy network contains only $\mathrm{10,628}$ trainable parameters, and inference was measured on a Cortex-M7 microcontroller at $598.7$ µs per step, $15.0\%$ of the 4 ms control period. Full article

A two-level closed-loop calibration framework is proposed to reduce odometry scale errors during motion and pose drift during stationary periods in Mecanum-wheeled mobile robots. At the upper calibration level, the planar displacement between the initial and final poses is calculated using the L2-norm, which reduces the influence of lateral deviation on distance measurements based on a single coordinate axis. Rotational displacement is obtained by accumulating normalized angular increments, thereby avoiding discontinuities when the yaw angle crosses the ±π boundary. A relay controller with a tolerance deadband is also introduced to reduce static-friction-induced stalling and oscillation near the target during low-speed calibration. At the lower odometry interface, the covariance assigned to wheel odometry measurements is adjusted according to the commanded zero-velocity state. During stationary periods, this adjustment increases the contribution of near-zero velocity measurements and limits the effect of residual velocity estimates and sensor noise on the fused pose. The identified longitudinal and rotational compensation factors are then updated online in the dead-reckoning node through an ROS 2 service. Unlike conventional ZUPT implementations, the proposed method does not require an additional zero-velocity pseudo-measurement node. Experiments were conducted on three near-horizontal surfaces: ceramic tile, epoxy resin, and asphalt. Across 720 bidirectional in-place rotation trials, the angular Error Reduction Rate ranged from (59.13%) to (96.58%). In 540 straight-line trials covering nine combinations of surface type and target distance, the overall mean absolute error decreased from 53.22 mm before calibration to 9.69 mm after calibration. Intermittent stop-and-go experiments were further performed using the EKF, UKF, RCKF, and a graph-based SLAM optimization framework implemented by slam_toolbox. For each estimation back-end, the estimated trajectory was evaluated by calculating its deviation from the corresponding synchronized /odom trajectory under the fixed-covariance and proposed ZUPT-based adaptive-covariance configurations; /odom was used as a common comparison baseline rather than as an absolute localization ground truth. The adaptive covariance strategy reduced the positional RMSE by (19.38%–67.44%) across the evaluated filtering back-ends. These results show that the proposed framework can reduce both motion-dependent odometry scale errors and stationary pose drift under the tested surface conditions. Full article

Safety Service Patrols (SSPs) are highway assistance programs that provide rapid response to crashes, disabled vehicles, and other incidents, helping to reduce congestion and improve roadway safety. Effective SSP coverage planning is important because timely deployment can facilitate quicker incident clearance and reduce the potential for secondary crashes. However, current SSP operations often rely on fixed patrol routes, which may not fully reflect where crash concentrations vary across time, weather, lighting, and roadway surface conditions. This study develops a data-driven framework to identify candidate priority SSP routes using historical crash records. Major interstate corridors in New Jersey, US, within the SSP coverage area were divided into uniform 1-mile roadway segments, and crashes were matched to segments using spatial proximity, roadway name, road system classification, and travel direction. Segment-level crash frequencies were then evaluated under 1152 scenario combinations defined by time of day, day of week, season, surface condition, weather condition, and lighting condition. Each corridor was represented as a directed graph, and feasible direction-specific routes were ranked based on cumulative crash frequency. The results show that crash concentration is spatially clustered, direction-dependent, and sensitive to operational conditions. Some candidate routes remained stable across scenarios, while others shifted with changes in weather, lighting, surface condition, and temporal period. Validation of the proposed route identification framework using recent crash data produced route overlap scores (ROS) of approximately 74 to 95 percent, indicating that many historically identified high-crash routes remained spatially consistent in the validation period. The framework provides a practical planning tool for SSP route reassessment, shift-level planning, and scenario-based patrol prioritization within existing service areas. Full article

University technology campuses contain specialized laboratories, academic programs, and services that can be difficult for first-time visitors to identify. This paper presents INDI, a custom mobile campus guide robot that combines spoken interaction, synthesized speech, touchscreen feedback, animated facial states, head motion, and predefined mobile guidance behaviors. The platform retains the modular mechanical concept of an earlier prototype while replacing its Raspberry Pi and open-loop remote-control architecture with an NVIDIA Jetson Nano, an Arduino Uno motor-control bridge, ROS 1 nodes, encoder feedback, and dual PID speed loops. The robot weighs 2.37 kg, measures $39\times 27\times 69.5$ cm, reaches a software-limited maximum speed of 0.4 m/s, and provides 27 min of continuous operation in the reported tests. Ten repetitions of each motion test produced mean displacements of 1.058 m and 2.182 m for 1 m and 2 m commands, respectively, and mean rotations of 89.2° and 180.3° for 90° and 180° commands. Voice trials achieved 90% correct interaction in a quiet environment and 70% under nearby conversational noise. In an exploratory user study with 13 participants, 16 of 20 assigned tasks were completed and the mean overall rating was 4.31/5. The results demonstrate the feasibility of an integrated, modular, physically embodied information service, while also revealing accumulated linear-motion error, sensitivity to ambient speech, limited battery duration, and the need for grounded institutional knowledge and autonomous localization. These findings are presented as preliminary evidence of technical and interaction feasibility rather than as confirmatory evidence of usability or campus-scale autonomous navigation. Full article

As tourism shifts from sightseeing to experience-oriented consumption, understanding how tourist perception differs across heterogeneous destination types and spatial scales remains challenging. Using 23,439 Volunteered Geographic Information (VGI) reviews archived for six destinations in Shangrao, China, this study compares mountain-type and rural comprehensive destination products. These are operational dominant-function categories rather than mutually exclusive geomorphological classes. The archive supports fine-grained sentiment, topic, and semantic-network analyses; annual temporal comparisons use the full 23,439-review corpus covering 2019–2025, whereas a separate subset of reviews posted from 1 August 2022 with official IP labels, aggregated into 2022–2024 province–year observations, supports Pooled Ordinary Least Squares (Pooled OLS) estimation. A hybrid lexicon–XLM-RoBERTa workflow, BERTopic, semantic co-occurrence analysis, and Pooled OLS are integrated in a cross-scale framework. Static results reveal shared strengths and weaknesses—high scenery and overall-experience evaluations but low price evaluations—alongside type-specific structures: mountain reviews concentrate on natural scenery, climbing effort, and accessibility, whereas rural reviews span village landscapes, cultural activities, accommodation, and nighttime experiences. Temporally, mountain demand retains a stable scenic core while accessibility concerns become more salient; rural demand shifts from traditional agricultural landscapes toward nighttime performances and other experience-oriented products. Cross-scale regressions identify destination- and dimension-specific correlates rather than causal drivers: urbanization is positively associated with several rural evaluations, while ecological contrast, climatic difference, and competing scenic resources are associated with more critical assessments in selected dimensions. The findings show that perception differences arise from the interaction of destination product structures and origin-region contexts, supporting differentiated accessibility management for mountain destinations and balanced product innovation, service improvement, and commercialization control for rural destinations. Full article

Renewable-powered agricultural water systems are often assessed as solar-pumping devices, but their sustainability depends on a service chain linking crop-water demand, hydraulic duty point, power electronics, storage, water quality, governance, operation and end-of-life management. This structured integrative review synthesizes peer-reviewed and practice-oriented evidence on photovoltaic pumping, hybrid renewable irrigation, grid-interactive pumps, micro-hydro assistance and renewable-powered brackish-water reverse osmosis (PV-RO). Evidence was screened across four source families and coded by service function, energy architecture, hydraulic duty and dominant sustainability pathway; recurring combinations were consolidated using explicit separation and merge rules. It develops an archetype-based screening framework for ex ante appraisal of irrigation, desalination and circularity risks. Seven technology-service archetypes are identified: direct PV pumping, PV-to-tank pumping, PV with electrical buffering, grid-interactive PV pumping, PV–wind hybrid irrigation, micro-hydro-assisted irrigation and PV-RO water making. The framework links each archetype to its operating envelope, evidence maturity, enabling subsystems, sustainability pathways, minimum indicators and ordinal triggers for deeper due diligence. Hydraulic storage is usually the lowest-regret reliability buffer for open-field irrigation, whereas batteries are justified mainly when pressure stability, fertigation timing or night-time operation has high agronomic value. PV-RO is a distinct water-making archetype and is environmentally defensible only where feed-water characterization, energy recovery, pretreatment, product-water agronomy, membrane management and permitted concentrate disposal are embedded in design. Two synthetic applications demonstrate archetype selection and due-diligence escalation. Responsible deployment requires service-oriented screening that integrates hydraulic design, groundwater governance, procurement quality assurance, circularity obligations and social inclusion before field implementation. Full article

Background/Objectives: Rehabilitation robotics is increasingly used in neurorehabilitation, yet evidence on how robotic services are organized, maintained and scaled in routine care remains fragmented. This mapping review examined service-delivery models, implementation determinants, strategies, outcomes, workflows and resource signals for robotic rehabilitation in service-linked clinical care. Methods: Six search sources were queried from inception to 21 February 2026. Eligible reports were studies addressing rehabilitation robotics within clinical pathways and reporting an implementation-relevant element. Data from 55 studies were systematically charted and mapped to CFIR determinants, ERIC strategies and Proctor implementation outcomes. Design-appropriate appraisal was applied once per included report to contextualize methodological confidence. Results: Staffing and supervision were reported in 50 studies (90.9%), workflow and scheduling in 43 (78.2%), safety governance in 32 (58.2%) and training and competency in 29 (52.7%). Feasibility was mapped in 40 studies (72.7%), acceptability in 31 (56.4%) and adoption in 23 (41.8%), whereas sustainability, penetration, technical support, documentation infrastructure and implementation cost were less consistently reported. Two randomized trials were appraised with RoB 2 and four non-randomized comparative studies with ROBINS-I; the remaining 49 reports received design-appropriate methodological or reporting appraisal. Conclusions: Routine-care rehabilitation robotics is best understood as a service configuration rather than a device alone. Transferable implementation requires explicit reporting of workforce, workflow, safety, documentation, technical support, infrastructure, cost, equity and sustainment. Frequencies indicate reporting presence, not practical importance or causal influence. The proposed workflow, logic model and minimum reporting set are evidence-informed, inductively derived proposals requiring prospective validation. Full article

Objective: The long-term management of cardiovascular diseases (CVDs) requires comprehensive rehabilitation strategies aimed at optimizing functional recovery and promoting behavioral changes. These interventions may include digital technologies that emerged as promising approaches to facilitate lifestyle changes, such as dietary behavior and physical activity (PA). Aim: This review aimed to synthesize the current evidence for the potential impact of remotely delivered nutrition interventions with or without a combination of PA interventions on biochemical biomarkers, cardiovascular indexes, anthropometric parameters, levels of PA, exercise and functional capacity, adherence to the Mediterranean Diet (MD), diet quality and quality of life, rehospitalization, urgent visits, death and acute events in adults (≥18 years) with CVDs. Secondary outcomes were accessibility, safety, usability, and adherence. Methods: The PubMed, Web of Science, and Scopus databases were comprehensively searched up to 2026 for Randomized Controlled Trials (RCTs) published in English, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The methodological quality was assessed using the revised Cochrane Risk of bias tool for RCTs (RoB2). Results: 11 RCTs met the inclusion criteria. Sample sizes ranged from 66 to 879 participants. The duration of the interventions varied from 4 weeks to 12 months. The interventions were delivered remotely using a range of digital health technologies, including web-based programs, mobile phones, wearable devices, smartphone applications, and mini-apps. Favorable changes were observed in diet quality and in adherence to MD and PA levels, with no intervention-related adverse events reported. The RoB2 assessment indicated that the majority of included studies (6 out of 11) were classified as having some concerns regarding risk of bias. Conclusions: Remotely combined nutrition and PA interventions are a feasible and effective approach for improving mainly patient-reported outcomes in adults with CVDs. These findings support integrating remote multimodal rehabilitation into routine cardiovascular care and highlight the potential of telehealth-based models to enhance patients’ access to rehabilitation services. Full article

Autonomous mobile robots require robust perception and high-level reasoning to operate in complex indoor environments. While the Robot Operating System (ROS) provides a modular framework for mapping and navigation, classical pipelines lack semantic understanding and natural-language interaction. This paper presents a semantic-aware autonomous navigation framework implemented on a ROS 1 (Melodic) mobile robot equipped with a two-dimensional light detection and ranging (LiDAR) sensor and an RGB-D camera. The system integrates LiDAR-based simultaneous localization and mapping (SLAM), the ROS navigation stack (move_base), and a lightweight You Only Look Once (YOLO) object detector for real-time on-board perception, and it anchors detections into the metric map to build a semantic map. A large language model (LLM) interprets natural-language instructions and converts them into structured navigation goals. Perception and control run entirely on-board the Jetson Nano, whereas the LLM is invoked episodically as a cloud service, yielding a hybrid embedded/cloud architecture. In indoor trials over a semantic map of two object classes, the system grounded all ten multilingual commands to the correct objects at a reasoning cost of about one second, and safely rejected a command referring to an unmapped object. We present this as an in-depth single-platform case study: owing to the 4 GB memory budget, the on-board detector and the full navigation stack are time-multiplexed rather than run continuously in parallel; nonetheless, a single degraded end-to-end trial confirmed that perception, online semantic anchoring, language grounding, and navigation compose within one continuous session. The framework offers a low-cost, extensible basis for language-guided robots in smart environments. Full article

Demand-Responsive Transport (DRT) has emerged as a flexible public transport strategy to improve accessibility, service coverage, and operational efficiency in contexts where conventional fixed-route services are inefficient, insufficient, or difficult to operate. In parallel, Artificial Intelligence (AI), and particularly Machine Learning (ML), has increasingly been investigated for predictive tasks relevant to DRT planning and operations, including demand forecasting, travel-time estimation, service reliability assessment, and decision support. This study presents a systematic literature review of ML-based predictive analytics in public-transport-oriented DRT services. After the screening and eligibility process, the included studies were analyzed according to predictive task, data source, modelling technique, validation strategy, performance metrics, and implementation-related challenges; methodological quality, risk of bias (RoB), and applicability were assessed using a PROBAST+AI-based framework. The results show that research in this field has expanded rapidly in recent years and is moving from isolated demand prediction models towards more integrated frameworks linking prediction, optimization, and service planning. However, the evidence remains fragmented, with substantial heterogeneity in data sources, spatial and temporal scales, modelling approaches, and evaluation procedures. The quality assessment showed generally favorable predictor quality and low outcome-related RoB, but analysis-related RoB was high in just over half of the studies, mainly because independent evaluation and validation accounting for temporal, spatial, or simulation-induced dependence were often lacking. Most studies provided retrospective, offline, simulation-based, or conceptual decision-support evidence, whereas prospective field deployment, external validation, and post-implementation monitoring were rarely or insufficiently documented. This review therefore provides a structured synthesis of current ML research in DRT and identifies priorities for future work, including improved reproducibility, stronger validation, robust baseline comparisons, multiple evaluation metrics, greater interpretability, and prospective assessment under real-world operational conditions. Full article

Background/Objectives: Romania’s National Health Insurance House (CNAS) reimbursement basket is the principal public financial-protection mechanism, yet whether it reflects the therapeutic services patients actually seek has not been tested against private-market offerings. This study compared the public basket with the private dental service portfolio, using the latter as an indirect, supply-side signal of expressed need rather than a direct measure of demand. Methods: In this cross-sectional study (November 2024–February 2025), the publicly listed portfolios of 420 private dental practices in Bucharest (n = 256), Cluj-Napoca (n = 104), and Iași (n = 60) were analyzed, stratified by size (0–3 vs. >3 employees). Practices were identified from CAEN 8623 records on listafirme.ro; portfolios were extracted from official websites displaying prices. Services were classified as functional or aesthetic by two independent raters (Cohen’s κ = 0.91), with disagreements resolved by consensus. Results: Across all municipalities and strata, approximately 53–68% of private services fell outside the CNAS basket. Critically, about 95–96% of private services were functional rather than aesthetic (4–5%), indicating that the gap reflects functional treatments patients seek and providers offer yet that remain unfunded—not elective demand. No statistically significant geographic differences emerged. Geographic location was not associated with portfolio breadth, whereas in multivariable models larger practices (>3 employees) independently offered approximately 33% more services. Conclusions: Because the analysis reflects advertised portfolio breadth rather than utilization, and a documented Romanian orientation toward reactive over preventive care, these hypothesis-generating findings make private supply a pragmatic, supply-side reference point for evidence-based revision of the CNAS basket, pointing to two directions directly supported by the data—aligning coverage with observed demand and administrative simplification of contracting. To our knowledge, this is the first benchmarking of the CNAS basket against private supply, relevant to other predominantly private European dental systems. Full article

Multimodal online reviews, which integrate textual content with user-uploaded images, have emerged as a significant information source for consumer decision-making and enterprise service enhancement in electronic commerce. Sarcastic reviews, characterized by positive textual statements that are contradicted by negative visual evidence, systematically distort sentiment analysis and subsequent evaluations. Most existing research on multimodal sarcasm detection is limited to binary classification. In contrast, many conventional review-based decision models permit cross-criterion trade-offs, allowing favorable performance on secondary attributes to obscure deficiencies in tolerance-sensitive attributes. To bridge the gap between misleading review content and reliable decision support, this study introduces an integrated three-module framework, the RDSF-MSD-SC. The first module, the Deep Feature Fusion Sarcasm Detection Model (DFF-SDM), combines RoBERTa-based contextual text analysis with CLIP-based cross-modal alignment to detect reviews in which linguistic and visual cues diverge. The second module, the Large Language Model-Driven Structured Semantic Correction Mechanism (LLM-SSCM), reinterprets identified sarcastic reviews and translates their implicit negative sentiment into structured evaluation units (problem dimension, problem severity, and correction confidence) that can be directly utilized by decision models. The third module, Non-Compensatory Decision-Making based on Contextual Tolerance and Mismatch Rate (NCDM-CTMR), aggregates the resulting severity matrix using context-specific tolerance vectors, penalizing breaches of tolerance rather than averaging them out. An empirical analysis of 8623 multimodal reviews from eight major U.S. passenger airlines demonstrates that DFF-SDM achieves an F1-score of 0.86, LLM-SSCM attains an expert-assessed correction quality score of 0.92, and NCDM-CTMR identifies Delta as the most context-appropriate airline with zero mismatch in a time-sensitive business travel scenario. Delta maintained the top ranking in 99.82% of the 20,000 Monte Carlo simulations conducted under a simplified mismatch formulation. Ablation analysis showed that the full pipeline recovered negative evidence from approximately 87% of the 2847 expert-labeled sarcastic reviews, a rate bounded by DFF-SDM’s recall of 0.87; the no-correction and no-detection variants recovered approximately 0%. Compared to the WSM, TOPSIS, and VIKOR baselines, the proposed framework yields a more threshold-sensitive differentiation among mid-tier alternatives. Full article

Educational environments, particularly those with limited resources, require affordable mobile robots capable of combining human–robot interaction, autonomous assistance, and academic support without continuous dependence on cloud services. This work presents a low-cost ROS2-based mobile robot implemented on a Raspberry Pi 4B to provide educational assistance in Spanish within controlled classroom environments. The system integrates voice interaction, text-to-speech synthesis, YOLOv8n-based object perception, a specialized door detection model, ultrasonic and inertial sensing, differential-drive control, and a hybrid natural language processing architecture based on semantic caching, local inference, and optional cloud connectivity. Two task-dependent operating modes, education and navigation, selectively activate ROS2 nodes to reduce computational load and energy consumption. Experimental tests conducted in a university classroom evaluated speech recognition, vision models, natural language processing alternatives, sensor behavior, and battery life. The speech recognition module achieved 98% accuracy under both quiet and noisy conditions. YOLOv8n achieved an F1-score of 0.975 for common classroom objects, while the specialized door detector achieved 100% recall with 58.7% precision. The semantic cache correctly resolved recurrent academic queries in the exact-match evaluation, with an average latency of 3.8 s, reducing the need for external language models in known-question scenarios. The robot operated for 96 min in education mode and 75.6 min in navigation mode. These results demonstrate that Spanish voice interaction, reactive navigation, academic question answering, and resource-aware operation can be integrated into a single low-cost edge robotic platform for educational environments. Full article

Next-generation communication technologies are increasingly shaping not only network infrastructure and digital services, but also public expectations, risk perceptions, and policy debates. As 5G deployment continues and 6G research accelerates, social responses to communication technologies have arguably become an important dimension of technology adoption, governance, and regulatory decision-making. Social media platforms provide timely and large-scale data sources for public opinion analysis. However, 5G/6G-related discourse often contains domain-specific terminology, technical complaints, and complex emotional expressions, which pose challenges for sentiment analysis. To address this challenge, this study constructs a manually annotated dataset of 1746 5G/6G-related Twitter posts collected across multiple communication-related events. This study aims to provide a domain-specific empirical evaluation of sentiment analysis models by examining classification performance, deployment-oriented inference efficiency, and lightweight domain adaptation. Three sentiment analysis methods are evaluated: twitter–roberta–base–sentiment, bertweet–base–sentiment–analysis, and VADER. In addition, a filtered Amazon Reviews’23 subset is used as an external review-style dataset, and a LoRA-based fine-tuning experiment is performed on Twitter-RoBERTa to examine domain adaptability. The results show that pre-trained language models achieve stronger classification performance than the rule-based method, particularly for domain-specific and semantically complex texts. VADER, by contrast, shows high observed efficiency under its CPU-based deployment setting, especially for short-text inference. The LoRA fine-tuned RoBERTa model further improves classification performance on both Twitter and Amazon test sets, indicating that lightweight parameter-efficient adaptation can enhance model robustness in specialized 5G/6G discourse. These findings contribute a domain-specific dataset, a deployment-oriented comparison of sentiment analysis paradigms, and empirical evidence on lightweight domain adaptation for 5G/6G-related public opinion monitoring. Full article

As global power systems transition toward increasing penetration of renewable energy sources (RESs), such as solar and wind, maintaining frequency stability in converter-dominated low-inertia grids has become a critical challenge. This review examines the role of inertia in power system dynamics, emphasising the consequences of reduced mechanical inertia, the resulting increase in the rate of change of frequency (RoCoF), and the associated stability risks in grids with high inverter-based penetration. Inertial, primary, and secondary frequency response mechanisms are discussed alongside potential cascading failures, protection system triggering, and pathways toward fully renewable grids are assessed. Virtual inertia techniques, including synchronverters, swing-equation-based methods, virtual synchronous generators (VSGs), droop control, Virtual Oscillator Control (VOC), and matching control, are evaluated in terms of benefits, limitations, implementation complexity, and Technology Readiness Levels (TRLs). A key contribution is a multi-criteria evaluation framework that classifies these methods by control adaptability, scalability, and communication requirements, providing system operators with a structured basis for strategy selection. A comparative assessment of Phase-Locked Loop (PLL) synchronisation methods, including SRF-PLL, DDSRF-PLL, FLL-PLL, and Kalman filter-based approaches, is presented under weak-grid, unbalanced, and harmonic-distorted conditions. The integration of virtual inertia with energy storage technologies, such as batteries, supercapacitors, and flywheels, is also discussed, along with its role as an ancillary service within evolving electricity markets and grid codes. Collectively, this study provides a unified reference to advance intelligent, scalable, and deployment-ready frequency control in low-inertia renewable power systems, offering both theoretical insights and practical guidance for future high-RES grid architectures. Full article